Although we presented a cost-effective approach for path following and an efficient so- lution for tracking dynamic objects as well as some mechanisms to improve the mapping accuracy, there are a number of possible extensions of this thesis. One promising direc- tion is the investigation of an outlier-removal approach to improve the mapping accuracy by removing the ghost detections of RFID tags caused by environmental effects, since the ghost detections bring a large amount of uncertainty to the state estimation and lead to mapping failures.
Due to the hardware limitations, only two RFID antennas are utilized for localizing the 3D positions of RFID tags throughout this thesis. In all experiments, the antennas are fixed on the robot during the operation. Due to the lack of distinct measurements, the error of height estimation is usually larger than the estimations in x and y directions. Therefore, it would be interesting to see if the 3D mapping accuracy can be improved by fusing the measurements from multiple antennas installed at different heights on the robot.
The RFID reader only provides a coarse measurement about the position of the tag. Therefore, the obvious extension of this thesis is the fusion with the metric measurements obtained from other sensors equipped on the robot, such as cameras or laser range finders, to improve the mapping accuracy. In this case, the object detection or recognition process can be further facilitated by taking advantages of both techniques.
For the path following, we assume a static configuration of the environment, i.e. the RFID tags are affixed to the immobile items or the walls. In practice, the items are not always static and may be relocated or removed by people. In this context, we plan to test our path following approach in dynamic environments.
Furthermore, one could combine the work described in Chapter 5 and Chapter 6 for the navigation of a robot with regard to a topological map in large unknown environ- ments. The nodes in the topological graph can be represented by a single RFID tag
or the RFID fingerprint. The tracking solution presented in this thesis allows the robot to navigate towards static RFID tags as well as dynamic ones and the path following approach we proposed enables the robot to travel along the path defined by RFID fin- gerprints autonomously. Both approaches have their weaknesses and advantages. The advantage of the tracking solution is the capability of coping with dynamic tags and an easier integration with obstacle avoidance, but suffers from the uncertainty of the tag position estimation caused by many environmental factors. By means of fingerprints, the path following approach does not rely on the distribution of the tags and is shown to be robust against the location-specific distortions that challenge our tracking approach, but the obstacle avoidance is not yet addressed. Therefore, it is interesting to combine these two techniques in order to achieve a robust and efficient navigation approach.
Our current strategy chooses the shortest path to the goal, while the robot moves to- wards the target during the tracking in this thesis. Therefore, an extension of this thesis is to use heuristic control algorithms in order to quickly move towards the object and mean- while obtain the maximum information gain (i.e. best reading rate of the tag attached to the object).
Obviously, the applications of our approaches are not limited to the fields mentioned in this thesis. Recently, manipulation capabilities of a mobile system have been widely addressed in industrial environments and everyday life. Moreover, low-cost embedded mobile RFID devices are popular in the market and the industry nowadays. Therefore, we believe that the approaches proposed in this thesis can be applied in a broad area of applications.
Alghamdi, S. and van Schyndel, R. (2012). Accurate positioning based on a combination of power attenuation and a signal strength indicator using active RFID technology. In
Third International Conference on Indoor Positioning and Indoor Navigation, pages
1–4, Sydney, Australia.
Alghamdi, S., van Schyndel, R., and Khalil, I. (2013). Accurate positioning using long range active RFID technology to assist visually impaired people. Journal of Network
and Computer Applications.
Alippi, C., Cogliati, D., and Vanini, G. (2006). A statistical approach to localize pas- sive RFIDs. In 2006 IEEE International Symposium on Circuits and Systems (ISCAS
2006), pages 843–846, Island of Kos, Greece.
Arulampalam, M., Maskell, S., Gordon, N., and Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-gaussian Bayesian tracking. Signal Processing, IEEE
Transactions on, 50(2), 174–188.
Azzouzi, S., Cremer, M., Dettmar, U., Knie, T., and Kronberger, R. (2011a). Improved AoA based localization of UHF RFID tags using spatial diversity. In 2011 IEEE
International Conference on RFID-Technologies and Applications (RFID-TA), pages
174–180.
Azzouzi, S., Cremer, M., Dettmar, U., Kronberger, R., and Knie, T. (2011b). New mea- surement results for the localization of UHF RFID transponders using an angle of arrival (AoA) approach. In 2011 IEEE International Conference on RFID, pages 91– 97.
Bahl, P. and Padmanabhan, V. (2000). Radar: An in-building RF-based user location and tracking system. In Proc. of the Nineteenth Annual Joint Conf. of the IEEE Com-
puter and Communications Societies (INFOCOM 2000), volume 2, pages 775–784,
Tel Aviv, Israel.
Balanis, C. A. (2005). Antenna Theory: Analysis and Design. John Wiley & Sons, Inc. Boccadoro, M., Martinelli, F., and Pagnottelli, S. (2010). Constrained and quantized
Kalman filtering for an RFID robot localization problem. Autonomous Robots, 29(3- 4), 235–251.
Borenstein, J., Feng, L., and Borenstein, C. J. (1996). Measurement and correction of systematic odometry errors in mobile robots. IEEE Transactions on Robotics and
Automation, 12, 869–880.
Burgard, W., Fox, D., and Hennig, D. (1997). Fast grid-based position tracking for mobile robots. In In Proc. of the 21th German Conference on Artificial Intelligence, pages 289–300. Springer Verlag.
Burgard, W., Derr, A., Fox, D., and Cremers, A. (1998). Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approach. In Proc. of the IEEE/RSJ International Conference on Intelligent Robots
and Systems.
Chawla, K., Robins, G., and Zhang, L. (2010). Object localization using RFID. In the
5th IEEE International Symposium on Wireless Pervasive Computing (ISWPC 2010),
pages 301–306, Modena, Italy.
Chawla, K., McFarland, C., Robins, G., and Shope, C. (2013). Real-time RFID local- ization using RSS. In Proc. of the 2013 International Conference on Localization and
GNSS (ICL-GNSS 2013), pages 1–6, Turin, Italy.
Choi, J. S., Lee, H., Engels, D., and Elmasri, R. (2012). Passive UHF RFID-based localization using detection of tag interference on smart shelf. Systems, Man, and
Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 42(2), 268–
275.
Cover, T. M. and Thomas, J. A. (1991). Elements of Information Theory. Wiley- Interscience, New York, NY, USA.
Deyle, T. (2011). Ultra High Frequency (UHF) Radio-Frequency Identification (RFID)
for Robot Perception and Mobile Manipulation. Ph.D. thesis, Georgia Institute of
Technology.
Deyle, T., Anderson, C., Kemp, C. C., and Reynolds, M. S. (2008a). A foveated pas- sive UHF RFID system for mobile manipulation. In 2008 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS 2008), pages 3711–3716. IEEE.
Deyle, T., Kemp, C., and Reynolds, M. (2008b). Probabilistic UHF RFID tag pose estimation with multiple antennas and a multipath RF propagation model. In Proc. of
the 2008 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2008), pages
1379–1384, Nice, France.
Deyle, T., Nguyen, H., Reynolds, M., and Kemp, C. (2009). Rf vision: RFID receive signal strength indicator (RSSI) images for sensor fusion and mobile manipulation. In
Proc. of the 2009 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2009),
Douc, R. and Cappe, O. (2005). Comparison of resampling schemes for particle filtering. In Image and Signal Processing and Analysis, 2005. ISPA 2005. Proceedings of the
4th International Symposium on, pages 64–69.
Doucet, A., Godsill, S., and Andrieu, C. (2000). On sequential monte carlo sampling methods for Bayesian filtering. STATISTICS AND COMPUTING, 10(3), 197–208. Dudek, G. and Jenkin, M. (2000). Computational Principles of Mobile Robotics. Cam-
bridge University Press, New York, NY, USA.
Ehrenberg, I., Floerkemeier, C., and Sarma, S. (2007). Inventory management with an RFID-equipped mobile robot. In Proc. of the 2007 IEEE Int. Conf. on Automation
Science and Engineering (CASE 2007), pages 1020–1026, Scottsdale, Arizona, U.S.A.
Eliazar, A. I. and Parr, R. (2004). Learning probabilistic motion models for mobile robots. In Proceedings of the Twenty-first International Conference on Machine Learn-
ing, ICML’04, pages 32–40, New York, NY, USA. ACM.
EPCglobal (2013). UHF Class 1 Gen 2 Standard v.2.0.0. Available on line: http://www.gs1.org/gsmp/kc/epcglobal/uhfc1g2.
Ferris, B., Hhnel, D., and Fox, D. (2006). Gaussian processes for signal strength-based location estimation. In In Proc. of Robotics Science and Systems.
Finkenzeller, K. (2003). RFID Handbook: Fundamentals and Applications in Contact-
less Smart Cards and Identification. John Wiley & Sons, Inc., New York, NY, USA, 2
edition.
Forster, C., Sabatta, D., Siegwart, R., and Scaramuzza, D. (2013). RFID-based hybrid metric-topological SLAM for GPS-denied environments. In Robotics and Automation
(ICRA), 2013 IEEE International Conference on, pages 5228–5234.
Fox, D. (2003). Adapting the sample size in particle filters through KLD-sampling.
International Journal of Robotics Research, 22(12), 985–1004.
Fox, D., Burgard, W., and Thrun, S. (1999). Markov localization for mobile robots in dynamic environments. Journal of Artificial Intelligence Research, 11, 391–427. Friedman, N., Murphy, K., and Russell, S. (1998). Learning the structure of dynamic
probabilistic networks. In Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI 1998), pages 139–147, San Francisco, CA, USA. Morgan
Kaufmann Publishers Inc.
Furgale, P. and Barfoot, T. (2010). Visual teach and repeat for long-range rover auton- omy. Journal of Field Robotics, 27(5), 534–560.
Germa, T., Lerasle, F., Ouadah, N., and Cadenat, V. (2010). Vision and RFID data fusion for tracking people in crowds by a mobile robot. Computer Vision Image Understand-
ing, 114(6), 641–651.
Gerold, K. (2007). Radio-frequency signal strength based localisation in unstructured
outdoor environments. Thesis, The University of Sydney, Australia.
Grisetti, G., Stachniss, C., and Burgard, W. (2005). Improving grid-based SLAM with Rao-Blackwellized particle filters by adaptive proposals and selective resampling. In IEEE International Conference on Robotics and Automation (ICRA 2005), pages 2443–2448, Barcelona, Spain.
Gueaieb, W. and Miah, M. (2009). A modular cost-effective mobile robot navigation system using RFID technology. Journal of Communications, 4(2), 89–95.
Gutmann, J.-S. and Fox, D. (2002). An experimental comparison of localization methods continued. In Proc. of the 2002 IEEE/RSJ Int. Conf. of Intelligent Robots and Systems
(IROS 2002), pages 454–459, EPFL, Switzerland.
Haeberlen, A., Flannery, E., Ladd, A. M., Rudys, A., Wallach, D. S., and Kavraki, L. E. (2004). Practical robust localization over large-scale 802.11 wireless networks. In
Proceedings of the 10th Annual International Conference on Mobile Computing and Networking (MobiCom 2004), pages 70–84, New York, USA.
H¨ahnel, D., Burgard, W., and Thrun, S. (2003). Learning compact 3D models of indoor and outdoor environments with a mobile robot. Robotics and Autonomous Systems, 44(1), 15–27.
H¨ahnel, D., Burgard, W., Fox, D., Fishkin, K., and Philipose, M. (2004). Mapping and localization with RFID technology. In Proc. of 2004 IEEE Int. Conf. on Robotics and
Automation (ICRA 2004), pages 1015–1020, USA. IEEE.
Hightower, J., Want, R., and Borriello, G. (2000). SpotON: An indoor 3D location sensing technology based on RF signal strength. Uw cse, University of Washington, Department of Computer Science and Engineering, Seattle, USA.
Hodges, S., Thorne, A., Mallinson, H., and Floerkemeier, C. (2007). Assessing and optimizing the range of UHF RFID to enable real-world pervasive computing applica- tions. In A. LaMarca, M. Langheinrich, and K. Truong, editors, Pervasive Computing, volume 4480 of Lecture Notes in Computer Science, pages 280–297. Springer Berlin Heidelberg.
Hoffmann, J., Spranger, M., Ghring, D., and Jngel, M. (2005). Making use of what you dont see: Negative information in markov localization. In Proc. of the 2005 IEEE/RSJ
Hori, T., Wda, T., Ota, Y., Uchitomi, N., Mutsuura, K., and Okada, H. (2008). A multi- sensing-range method for position estimation of passive RFID tags. In 2008 IEEE
International Conference on Wireless and Mobile Computing, Networking and Com- munications (WIMOB 2008), pages 208–213, Avignon, France.
Johansson, R. and Saffiotti, A. (2009). Navigating by stigmergy: A realization on an RFID floor for minimalistic robots. In Proc. of the IEEE Int. Conf. on Robotics and
Automation (ICRA), pages 245–252, Kobe, Japan.
Johnson, N. L., Kotz, S., and Balakrishnan, N. (1995). Continuous Univariate Distribu-
tions, volume 1. John Wiley & Sons, New York, NY.
Joho, D., Plagemann, C., and Burgard, W. (2009). Modeling RFID signal strength and tag detection for localization and mapping. In Proc. of 2009 IEEE Int. Conf. on Robotics
and Automation (ICRA 2009), pages 3160–3165, Kobe, Japan.
Julier, S. J. and Uhlmann, J. K. (1997). A new extension of the Kalman filter to nonlinear systems. Int. symp. aerospace/defense sensing, simul. and controls, 3(26).
Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Jour-
nal of Fluids Engineering, 82, 35–48.
K¨ampke, T., Kluge, B., Prassler, E., and Strobel, M. (2008). Robot position estimation on a RFID-tagged smart floor. In C. Laugier and R. Siegwart, editors, Field and
Service Robotics, volume 42 of Springer Tracts in Advanced Robotics, pages 201–
211. Springer Berlin Heidelberg.
Kanda, T., Shiomi, M., Perrin, L., Nomura, T., Ishiguro, H., and Hagita, N. (2007). Analysis of people trajectories with ubiquitous sensors in a science museum. In Proc.
of 2007 IEEE Int. Conf. on Robotics and Automation (ICRA 2007), pages 4846–4853,
Roma, Italy.
Kantor, G. A. and Singh, S. (2002). Preliminary results in range-only localization and mapping. In Proceedings of the IEEE Conference on Robotics and Automation (ICRA
2002), pages 1818–1823, Washington, USA.
Karmakar, N. C. (2011). Handbook of smart antennas for RFID systems.
Kim, M., Kim, H., and Chong, N. (2007). Automated robot docking using direction sensing RFID. In Proc. of the 2007 IEEE Int. Conf. on Robotics and Automation
(ICRA 2007), pages 4588–4593, Italy.
Kjærgaard, M. B. (2007). A taxonomy for radio location fingerprinting. In Location-and
Kleiner, A., Kleiner, E., Prediger, J., and Nebel, B. (2006). RFID technology-based exploration and SLAM for search and rescue. In In Proc. of the IEEE/RSJ Int. Conf.
on Intelligent Robots and Systems (IROS, pages 4054–4059, Beijing, China.
Kleiner, A., Dornhege, C., and Dali, S. (2007). Mapping disaster areas jointly: RFID- coordinated SLAM by humans and robots. In 2007 IEEE International Workshop on
Safety, Security, and Rescue Robotics (SSRR 2007), pages 1–6, Rome, Italy.
Kloos, G., Guivant, J., Nebot, E., and Masson, F. (2006). Range based localisation using RF and the application to mining safety. In 2006 IEEE/RSJ International Conference
on Intelligent Robots and Systems, pages 1304–1311, Beijing, China.
Kluge, B., Kohler, C., and Prassler, E. (2001). Fast and robust tracking of multiple moving objects with a laser range finder. In Proc. of 2001 IEEE Int. Conf. on Robotics
and Automation (ICRA 2001), pages 1683–1688, Seoul, Korea.
Koch, W. (2007). On exploiting negative sensor evidence for target tracking and sensor data fusion. Information Fusion, 8(1), 28–39.
Kodaka, K., Niwa, H., Sakamoto, Y., Otake, M., Kanemori, Y., and Sugano, S. (2008). Pose estimation of a mobile robot on a lattice of RFID tags. In 2008 IEEE/RSJ Interna-
tional Conference on Intelligent Robots and Systems (IROS 2008), pages 1385–1390.
Koller, D. and Fratkina, R. (1998). Using learning for approximation in stochastic pro- cesses. In In Proceedings of the 1998 International Conference on Machine Learning
(ICML, pages 287–295, Madison, Wisconsin, USA.
Kristan, M., Kovacic, S., Leonardis, A., and Pers, J. (2010). A two-stage dynamic model for visual tracking. IEEE Transactions on Systems, Man, and Cybernetics, Part B:
Cybernetics, 40(6), 1505–1520.
Kulyukin, V., Gharpure, C., Nicholson, J., and Pavithran, S. (2004). RFID in robot- assisted indoor navigation for the visually impaired. In Proc. of the 2004 IEEE/RSJ
Int. Conf. on Intelligent Robots and Systems (IROS 2004), pages 1979–1984.
Kulyukin, V., Gharpure, C., Nicholson, J., and Osborne, G. (2006). Robot-assisted wayfinding for the visually impaired in structured indoor environments. Autonomous
Robots, 21(1), 29–41.
Kwon, J. and Lee, K. M. (2010). Visual tracking decomposition. In The Twenty-Third
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010), pages
1269–1276, San Francisco, CA, USA.
Ladd, A., Bekris, K., Rudys, A., Kavraki, L., and Wallach, D. (2005). Robotics-based location sensing using wireless ethernet. Wireless Networks, 11(1-2), 189–204.
Lau, P.-Y., Yung, K.-O., and Yung, E.-N. (2008). A smart bookshelf for library RFID system. In The 2008 Asia-Pacific Microwave Conference (APMC 2008), pages 1–4, Hong Kong, China.
Lee, S., Min, B.-C., Kim, D., Yoon, J., and Kim, D. (2013). Passive RFID positioning system using RF power control. In J.-H. Kim, E. T. Matson, H. Myung, and P. Xu, ed- itors, Robot Intelligence Technology and Applications 2012, volume 208 of Advances
in Intelligent Systems and Computing, pages 845–853. Springer Berlin Heidelberg.
Lehpamer, H. (2007). RFID Design Principles. Artech House, Inc., Norwood, MA, USA.
Lenser, S. and Veloso, M. (2000). Sensor resetting localization for poorly modelled mobile robots. In Proc. of the 2000 IEEE Int. Conf. on Robotics and Automation
(ICRA 2000, pages 1225–1232, San Francisco, CA, USA.
Levis, C. A. (2001). Friis Free-Space Transmission Formula. John Wiley & Sons, Inc. Lilienthal, A. J., Loutfi, A., and Duckett, T. (2006). Airborne chemical sensing with
mobile robots. Sensors, 6(11), 1616–1678.
Lim, A. and Zhang, K. (2006). A robust RFID-based method for precise indoor posi- tioning. Advances in Applied Artificial Intelligence, pages 1189–1199.
Liu, J. and West, M. (2001). Combined Parameter and State Estimation in Simulation-
Based Filtering. Statistics for Engineering and Information Science. Springer New
York.
Liu, J. S. (1996). Metropolized independent sampling with comparisons to rejec- tion sampling and importance sampling. Statistics and Computing, 6, 113–119.
10.1007/BF00162521.
Liu, R. and Zell, A. (2014). Towards Localizing Both Static and Non-static RFID Tags with a Mobile Robot. In International Conference on Intelligent Autonomous Systems
(IAS-13), Padova, Italy.
Liu, R., Vorst, P., Koch, A., and Zell, A. (2011). Path following for indoor robots with RFID received signal strength. In Proc. of the 19th Int. Conf. on Software, Telecom-
munications and Computer Networks (SoftCOM 2011), Croatia.
Liu, R., Koch, A., and Zell, A. (2012). Path following with passive UHF RFID received signal strength in unknown environments. In Porc. of the 2012 IEEE/RSJ Int. Conf. on
Intelligent Robots and Systems (IROS 2012), pages 2250–2255, Vilamoura, Algarve,
Liu, R., Koch, A., and Zell, A. (2013). Mapping UHF RFID Tags with a Mobile Robot using 3D Sensor Model. In IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS 2013), pages 1589–1594, Big Sight, Tokyo, Japan.
Liu, R., Huski´c, G., and Zell, A. (2014). Dynamic Objects Tracking with a Mobile Robot using Passive UHF RFID Tags. In IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2014), Chicago, Illinois, USA.
Liu, X., Corner, M. D., and Shenoy, P. (2006). Ferret: RFID localization for pervasive multimedia. In Proc. of the 8th Int. Conf. on Ubiquitous Computing (UbiComp 2006), pages 422–440, Orange County, CA, USA.
Lu, F. and Milios, E. (1997). Robot pose estimation in unknown environments by match- ing 2D range scans. Journal of Intelligent & Robotic Systems, 18(3), 249–275. Maguire, Y. and Pappu, R. (2009). An Optimal Q-Algorithm for the ISO 18000-6C RFID
Protocol. IEEE Transactions on Automation Science and Engineering, 6(1), 16–24. Mallinson, H., Hodges, S., and Thorne, A. (2006). Determining a better metric for RFID
performance in environments with varying noise levels. In 12th IEEE International
Conference on Methods and Models in Automation and Robotics (MMAR 2006), pages
39–48. Miedzyzdroje, Poland.
Medeiros, C., Costa, J., and Fernandes, C. (2008). RFID smart shelf with confined detection volume at UHF. Antennas and Wireless Propagation Letters, IEEE, 7, 773– 776.
Mehmood, M. A., Kulik, L., and Tanin, E. (2008). Autonomous navigation of mobile agents using RFID-enabled space partitions. In Proceedings of the 16th ACM SIGSPA-
TIAL International Conference on Advances in Geographic Information Systems, GIS
’08, pages 1–21.
Mekonnen, A. A., Lerasle, F., and Herbulot, A. (2013). Cooperative passers-by tracking with a mobile robot and external cameras. Comput. Vis. Image Underst., 117(10), 1229–1244.
Miah, M. S. and Gueaieb, W. (2013). Mobile robot trajectory tracking using noisy RSS measurements: An RFID approach. ISA Transactions: The Journal of Automation,
Elsevier.
Milella, A., Cicirelli, G., and Distante, A. (2008). RFID-assisted mobile robot system for mapping and surveillance of indoor environments. Industrial Robot: An International
Milella, A., Di Paola, D., Cicirelli, G., and D’Orazio, T. (2009). RFID tag bearing estimation for mobile robot localization. In Proc. of the 2009 International Conference
on Advanced Robotics (ICAR 2009), pages 1–6, Munich, Germany.
Miller, T., Stolfo, D., and Spletzer, J. (2010). An automated asset locating system (AALS) with applications to inventory management. In A. Howard, K. Iagnemma, and A. Kelly, editors, Field and Service Robotics, volume 62 of Springer Tracts in
Advanced Robotics, pages 163–172. Springer Berlin Heidelberg.
Montemerlo, M., Roy, N., and Thrun, S. (2003). Perspectives on standardization in mobile robot programming: The carnegie mellon navigation (CARMEN) toolkit. In
In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS, pages
2436–2441, Las Vegas, Nevada, USA.
Moore, A. W., Schneider, J., and Deng, K. (1997). Efficient locally weighted polynomial regression predictions. In In Proceedings of the 1997 International Machine Learning
Conference (ICML), pages 236–244, Nashville, Tennessee, USA.
Mountney, P., Stoyanov, D., and Yang, G.-Z. (2010). Three-dimensional tissue defor- mation recovery and tracking: Introducing techniques based on laparoscopic or endo- scopic images. IEEE Signal Processing Magazine, 27(4), 14–24.
Nemmaluri, A., Corner, M. D., and Shenoy, P. (2008). Sherlock: Automatically locating objects for humans. In Proceedings of the 6th International Conference on Mobile
Systems, Applications, and Services, MobiSys ’08, pages 187–198, New York, NY,
USA. ACM.
Ni, L., Liu, Y., Lau, Y. C., and Patil, A. (2003). LANDMARC: indoor location sensing using active RFID. In Proc. of the 2003 IEEE Int. Conf. on Pervasive Computing and
Communications (PerCom 2003), pages 407–415, USA.
Ota, Y., Hori, T., Onishi, T., Wada, T., Mutsuura, K., and Okada, H. (2008). An adap- tive likelihood distribution algorithm for the localization of passive RFID tags. IEICE
Transactions A: IEICE Transactions on Fundamentals of Electronics, Communica- tions and Computer Sciences, E91-A(7), 1666–1675.
Otsason, V., Varshavsky, A., LaMarca, A., and De Lara, E. (2005). Accurate GSM indoor